Extracting melody from polyphonic audio is a fundamental and important task in the music information retrieval field. The aim is to extract the pitch of the dominant singing voice from polyphonic audio. There are many downstream applications of melody extraction, including music recommendation [1]}, cover song identification [2]}, music generation [3]} and voice separation [4]}. Deep learning models provide excellent performance when sufficiently large annotated data is available in the source domain. But this performance degrades when these models are applied to different target domains with a very different data distribution as compared to source domains. In the audio domain, a model trained on source domain data, for example, songs of a particular singer or genre, may not perform comparatively well in extracting melody from other target domain data. Domain adaptation [5]}[6]} caters to this problem, to enable the model trained on the source domain to perform well on the target domain. In this paper,
we apply domain adaptation methods to the problem of polyphonic melody extraction. In particular, we study fine-tuning and meta-learning-based domain adaptation approaches.
Meta-learning has been recently used to improve the performance of few-shot learning problems [7]}[8]}. The common approaches to meta-learning are metric-based [7]}, model-based [8]}, and optimization-based [11]} learning that improve the learning speed [12]}. To the best of our knowledge, no such work on meta-learning-based domain adaptation for polyphonic melody extraction has been conducted as per the literature.
